The Importance Of Analytics In The Public Sector

Shaily Kumar

Analytics is an important and rapidly developing business functionality that is used by increasing numbers of organizations around the world. Reflecting this trend, government agencies and departments are now taking the opportunity to leverage contextual real-time information into their decision-making processes.

Policy evaluations, education, and healthcare are examples of how government can use analyics to gain new levels of insight. There are many additional ways to apply analytics to decision-making in the public sector. Making well-informed decisions can save lives, enable government agencies to make more efficient and impactful investment decisions, and work towards improving the lives of citizens. Many government agencies already collect data and have comprehensive privacy guidelines, legal safeguards, and monitoring procedures that regulate how that data can be used. When data is not available or needs to be expanded, the government has many ways of accessing relevant subject matter expertise.

Historically, the use of analytics by government agencies has yielded several success stories. For example, the military has long been an innovator in analytics and was a pioneer in the field of operational research during the Second World War. Intelligent analysis, although what most business people would consider quantitative analysis, is already well integrated with decision-making. Agencies analyze the costs, benefits, and other effects of proposed regulations. In addition, the public sector has been leading in the development of analytics for performance management to measure the impact of hard-to-measure policy lead programs.

In another example, the Tucson sector of the U.S. Border Patrol (USBP), the busiest sector for aliens and drug trafficking along the border with Mexico, uses analytics to inform the allocation of resources. A review was conducted in anticipation of an additional US$600 million credit allocation granted by Congress and the president on the southwestern border in August 2010. The Tucson Sector USBP expected to receive part of these funds and the Tucson chief patrol agent needed to decide how to allocate the additional funds over a 262-mile limit, which is divided into zones, and between a range of countermeasures, including staff, screens, monitoring equipment, special units and forward operating bases.

In collaboration with a team of analysts from the Department of Homeland Security, the USBP conducted an assessment that included descriptive analysis to identify “hot spots” and gaps in sources and prescriptive analysis to link analytical conclusions to the alternatives of the decision-maker. Data collected from the files of the USBP and from subject-matter experts was then analyzed to identify the allowances against countermeasures that are expected to yield the best rate of return reduction in risk mitigation using statistical modeling. The assessment and findings were presented to the chief agent, who used it to allocate more than 1,000 USBP agents, place a term base, and then develop an operational plan for part of the sector.

This is just one of the many use cases where government agency has used analytics to create efficiencies. For more examples of analytics in the public sector, click here.

Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: Twitter: